If you’ve been following my Youtube channel, you know I’ve been doing machine learning tutorials on the cancer dataset that comes preloaded with scikit-learn in Python.

The tutorials follow a path similar to what’s in Andreas Muller and Sarah Guido’s book on machine learning. This allows me not only to pass on knowledge to others who are interested in the details, but also to strengthen my own knowledge of these concepts.

24 video tutorials in and I realize that this is going to be a long series. I want to take the appropriate time to look into the details and to apply the very specifics for each algorithm and concept as we explore the cancer dataset.

So, to easily navigate through, here’s the current list of tutorials, broken down into sections:

For convenience, I created a playlist of all the videos, which you can access here. And you can also download the notebooks used in the videos from the github repository I made for this project; for even more convenience, you can fork the repository.

If there’s a specific machine learning problem that you’d like me to approach, that is relevant to this, please let me know below. Also, if there’s something that appears unclear in any of the tutorials, do let me know so I can possibly further explain it better.